Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations13932
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory136.0 B

Variable types

Numeric15
Categorical2

Alerts

CNTR_DIST is highly overall correlated with LATITUDE and 5 other fieldsHigh correlation
LATITUDE is highly overall correlated with CNTR_DIST and 2 other fieldsHigh correlation
LONGITUDE is highly overall correlated with CNTR_DIST and 2 other fieldsHigh correlation
PARCELNO is highly overall correlated with CNTR_DISTHigh correlation
SALE_PRC is highly overall correlated with TOT_LVG_AREAHigh correlation
SUBCNTR_DI is highly overall correlated with CNTR_DISTHigh correlation
TOT_LVG_AREA is highly overall correlated with SALE_PRCHigh correlation
WATER_DIST is highly overall correlated with CNTR_DIST and 2 other fieldsHigh correlation
age is highly overall correlated with CNTR_DISTHigh correlation
avno60plus is highly imbalanced (88.8%) Imbalance
SPEC_FEAT_VAL has 2290 (16.4%) zeros Zeros
age has 1003 (7.2%) zeros Zeros

Reproduction

Analysis started2024-11-17 12:15:25.580383
Analysis finished2024-11-17 12:15:51.911810
Duration26.33 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

LATITUDE
Real number (ℝ)

High correlation 

Distinct13776
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.728811
Minimum25.434333
Maximum25.974382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:52.080279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.434333
5-th percentile25.485134
Q125.620056
median25.73181
Q325.852269
95-th percentile25.947349
Maximum25.974382
Range0.54004849
Interquartile range (IQR)0.23221249

Descriptive statistics

Standard deviation0.14063328
Coefficient of variation (CV)0.0054659845
Kurtosis-0.969276
Mean25.728811
Median Absolute Deviation (MAD)0.11667937
Skewness-0.094570331
Sum358453.8
Variance0.019777721
MonotonicityNot monotonic
2024-11-17T13:15:52.346164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.83753571 3
 
< 0.1%
25.81659054 3
 
< 0.1%
25.74963975 3
 
< 0.1%
25.90807992 3
 
< 0.1%
25.71937295 2
 
< 0.1%
25.86168199 2
 
< 0.1%
25.71296095 2
 
< 0.1%
25.85494095 2
 
< 0.1%
25.8861223 2
 
< 0.1%
25.94647111 2
 
< 0.1%
Other values (13766) 13908
99.8%
ValueCountFrequency (%)
25.43433337 1
< 0.1%
25.43575892 1
< 0.1%
25.43706849 1
< 0.1%
25.43775676 1
< 0.1%
25.4383722 1
< 0.1%
25.43837415 1
< 0.1%
25.43837629 1
< 0.1%
25.43842456 1
< 0.1%
25.43852239 1
< 0.1%
25.43877887 1
< 0.1%
ValueCountFrequency (%)
25.97438187 1
< 0.1%
25.97395801 1
< 0.1%
25.97379156 1
< 0.1%
25.97359426 1
< 0.1%
25.97355639 1
< 0.1%
25.97317637 2
< 0.1%
25.97268857 1
< 0.1%
25.9726663 1
< 0.1%
25.97249165 1
< 0.1%
25.97248511 1
< 0.1%

LONGITUDE
Real number (ℝ)

High correlation 

Distinct13776
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-80.327475
Minimum-80.542172
Maximum-80.119746
Zeros0
Zeros (%)0.0%
Negative13932
Negative (%)100.0%
Memory size109.0 KiB
2024-11-17T13:15:52.660497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80.542172
5-th percentile-80.449447
Q1-80.403278
median-80.338911
Q3-80.258019
95-th percentile-80.176288
Maximum-80.119746
Range0.42242571
Interquartile range (IQR)0.14525877

Descriptive statistics

Standard deviation0.089199072
Coefficient of variation (CV)-0.0011104429
Kurtosis-0.90812949
Mean-80.327475
Median Absolute Deviation (MAD)0.071942984
Skewness0.29259217
Sum-1119122.4
Variance0.0079564745
MonotonicityNot monotonic
2024-11-17T13:15:52.920352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.2288869 3
 
< 0.1%
-80.19838682 3
 
< 0.1%
-80.43151479 3
 
< 0.1%
-80.16795412 3
 
< 0.1%
-80.44264629 2
 
< 0.1%
-80.19670144 2
 
< 0.1%
-80.4448762 2
 
< 0.1%
-80.17721289 2
 
< 0.1%
-80.22574991 2
 
< 0.1%
-80.16067606 2
 
< 0.1%
Other values (13766) 13908
99.8%
ValueCountFrequency (%)
-80.5421721 1
< 0.1%
-80.54215815 1
< 0.1%
-80.53541025 1
< 0.1%
-80.53425471 1
< 0.1%
-80.53196902 1
< 0.1%
-80.53136452 1
< 0.1%
-80.53098823 1
< 0.1%
-80.52944659 1
< 0.1%
-80.52917238 1
< 0.1%
-80.52741485 1
< 0.1%
ValueCountFrequency (%)
-80.11974639 1
< 0.1%
-80.121107 1
< 0.1%
-80.12138187 1
< 0.1%
-80.12139013 1
< 0.1%
-80.12141131 1
< 0.1%
-80.12150487 1
< 0.1%
-80.12201303 1
< 0.1%
-80.1225595 1
< 0.1%
-80.12256411 1
< 0.1%
-80.12290527 1
< 0.1%

PARCELNO
Real number (ℝ)

High correlation 

Distinct13776
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1642637 × 10-311
Minimum5.0395092 × 10-313
Maximum1.8083643 × 10-311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:53.156010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.0395092 × 10-313
5-th percentile6.9692965 × 10-313
Q15.3317589 × 10-312
median1.5021078 × 10-311
Q31.511925 × 10-311
95-th percentile1.7441408 × 10-311
Maximum1.8083643 × 10-311
Range1.7579692 × 10-311
Interquartile range (IQR)9.7874909 × 10-312

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1.1642637 × 10-311
Median Absolute Deviation (MAD)1.4347741 × 10-313
Skewness0
Sum1.6220522 × 10-307
Variance0
MonotonicityNot monotonic
2024-11-17T13:15:53.366323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.49758711 × 10-3113
 
< 0.1%
6.484127447 × 10-3133
 
< 0.1%
1.506456198 × 10-3113
 
< 0.1%
1.493165206 × 10-3113
 
< 0.1%
1.506510008 × 10-3112
 
< 0.1%
5.593120195 × 10-3122
 
< 0.1%
1.506544562 × 10-3112
 
< 0.1%
6.52512795 × 10-3132
 
< 0.1%
3.069433055 × 10-3122
 
< 0.1%
3.567352281 × 10-3122
 
< 0.1%
Other values (13766) 13908
99.8%
ValueCountFrequency (%)
5.039509163 × 10-3131
< 0.1%
5.039963707 × 10-3131
< 0.1%
6.478188765 × 10-3131
< 0.1%
6.478192721 × 10-3131
< 0.1%
6.478192731 × 10-3131
< 0.1%
6.478194682 × 10-3131
< 0.1%
6.478212481 × 10-3131
< 0.1%
6.478222347 × 10-3131
< 0.1%
6.478685803 × 10-3131
< 0.1%
6.478687296 × 10-3131
< 0.1%
ValueCountFrequency (%)
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%
1.808364335 × 10-3111
< 0.1%

SALE_PRC
Real number (ℝ)

High correlation 

Distinct2111
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399941.93
Minimum72000
Maximum2650000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:53.609944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72000
5-th percentile145355
Q1235000
median310000
Q3428000
95-th percentile1019900
Maximum2650000
Range2578000
Interquartile range (IQR)193000

Descriptive statistics

Standard deviation317214.68
Coefficient of variation (CV)0.79315185
Kurtosis13.275156
Mean399941.93
Median Absolute Deviation (MAD)90000
Skewness3.2155058
Sum5.571991 × 109
Variance1.0062516 × 1011
MonotonicityNot monotonic
2024-11-17T13:15:53.855464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000 196
 
1.4%
300000 193
 
1.4%
260000 163
 
1.2%
270000 163
 
1.2%
280000 152
 
1.1%
290000 152
 
1.1%
350000 151
 
1.1%
265000 150
 
1.1%
285000 145
 
1.0%
210000 142
 
1.0%
Other values (2101) 12325
88.5%
ValueCountFrequency (%)
72000 4
< 0.1%
73000 1
 
< 0.1%
74900 1
 
< 0.1%
75000 8
0.1%
76000 4
< 0.1%
76800 1
 
< 0.1%
77000 1
 
< 0.1%
77800 1
 
< 0.1%
78000 3
 
< 0.1%
79000 2
 
< 0.1%
ValueCountFrequency (%)
2650000 4
< 0.1%
2639300 1
 
< 0.1%
2637500 1
 
< 0.1%
2625000 1
 
< 0.1%
2600000 7
0.1%
2599000 1
 
< 0.1%
2575000 1
 
< 0.1%
2550000 4
< 0.1%
2510000 1
 
< 0.1%
2500000 7
0.1%

LND_SQFOOT
Real number (ℝ)

Distinct4696
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8620.8799
Minimum1248
Maximum57064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:54.411414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1248
5-th percentile3761.65
Q15400
median7500
Q39126.25
95-th percentile17400
Maximum57064
Range55816
Interquartile range (IQR)3726.25

Descriptive statistics

Standard deviation6070.0887
Coefficient of variation (CV)0.70411475
Kurtosis19.076805
Mean8620.8799
Median Absolute Deviation (MAD)1928.5
Skewness3.8301152
Sum1.201061 × 108
Variance36845977
MonotonicityNot monotonic
2024-11-17T13:15:54.667022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 1021
 
7.3%
5000 627
 
4.5%
6000 312
 
2.2%
7875 187
 
1.3%
8250 136
 
1.0%
8000 129
 
0.9%
15000 121
 
0.9%
5500 114
 
0.8%
5250 110
 
0.8%
10000 109
 
0.8%
Other values (4686) 11066
79.4%
ValueCountFrequency (%)
1248 1
 
< 0.1%
1262 1
 
< 0.1%
1279 1
 
< 0.1%
1392 5
< 0.1%
1471 1
 
< 0.1%
1522 1
 
< 0.1%
1635 2
 
< 0.1%
1644 4
< 0.1%
1881 1
 
< 0.1%
1889 1
 
< 0.1%
ValueCountFrequency (%)
57064 1
 
< 0.1%
56192 2
 
< 0.1%
55757 3
 
< 0.1%
55321 1
 
< 0.1%
54886 3
 
< 0.1%
54450 10
0.1%
53579 2
 
< 0.1%
52272 1
 
< 0.1%
52102 1
 
< 0.1%
51836 1
 
< 0.1%

TOT_LVG_AREA
Real number (ℝ)

High correlation 

Distinct2978
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2058.0446
Minimum854
Maximum6287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:54.942754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum854
5-th percentile1087
Q11470
median1877.5
Q32471
95-th percentile3630
Maximum6287
Range5433
Interquartile range (IQR)1001

Descriptive statistics

Standard deviation813.53854
Coefficient of variation (CV)0.39529685
Kurtosis2.4872901
Mean2058.0446
Median Absolute Deviation (MAD)471.5
Skewness1.3469304
Sum28672677
Variance661844.95
MonotonicityNot monotonic
2024-11-17T13:15:55.200822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3079 29
 
0.2%
3199 27
 
0.2%
1440 24
 
0.2%
2176 22
 
0.2%
1701 22
 
0.2%
2578 21
 
0.2%
2193 21
 
0.2%
2091 21
 
0.2%
1606 20
 
0.1%
2514 20
 
0.1%
Other values (2968) 13705
98.4%
ValueCountFrequency (%)
854 3
< 0.1%
855 1
 
< 0.1%
858 2
< 0.1%
859 2
< 0.1%
860 4
< 0.1%
861 2
< 0.1%
862 4
< 0.1%
863 3
< 0.1%
864 1
 
< 0.1%
865 2
< 0.1%
ValueCountFrequency (%)
6287 1
< 0.1%
6261 1
< 0.1%
6251 1
< 0.1%
6236 1
< 0.1%
6171 1
< 0.1%
6137 1
< 0.1%
6074 1
< 0.1%
6026 1
< 0.1%
6016 1
< 0.1%
6014 1
< 0.1%

SPEC_FEAT_VAL
Real number (ℝ)

Zeros 

Distinct7583
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9562.4935
Minimum0
Maximum175020
Zeros2290
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:55.483476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1810
median2765.5
Q312352.25
95-th percentile38452.4
Maximum175020
Range175020
Interquartile range (IQR)11542.25

Descriptive statistics

Standard deviation13890.968
Coefficient of variation (CV)1.4526512
Kurtosis4.6672485
Mean9562.4935
Median Absolute Deviation (MAD)2765.5
Skewness1.9022502
Sum1.3322466 × 108
Variance1.9295899 × 108
MonotonicityNot monotonic
2024-11-17T13:15:55.734625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2290
 
16.4%
550 45
 
0.3%
440 27
 
0.2%
4800 23
 
0.2%
1200 22
 
0.2%
3200 21
 
0.2%
2240 20
 
0.1%
2200 20
 
0.1%
2460 19
 
0.1%
1296 18
 
0.1%
Other values (7573) 11427
82.0%
ValueCountFrequency (%)
0 2290
16.4%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 3
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
24 1
 
< 0.1%
29 2
 
< 0.1%
53 1
 
< 0.1%
ValueCountFrequency (%)
175020 1
< 0.1%
123590 1
< 0.1%
110895 1
< 0.1%
110238 1
< 0.1%
101214 1
< 0.1%
100780 1
< 0.1%
99616 1
< 0.1%
93967 1
< 0.1%
89797 1
< 0.1%
87918 1
< 0.1%

RAIL_DIST
Real number (ℝ)

Distinct13235
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8348.5487
Minimum10.5
Maximum29621.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:56.002571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile760.28
Q13299.45
median7106.3
Q312102.6
95-th percentile20243.705
Maximum29621.5
Range29611
Interquartile range (IQR)8803.15

Descriptive statistics

Standard deviation6178.0273
Coefficient of variation (CV)0.74001213
Kurtosis-0.012672868
Mean8348.5487
Median Absolute Deviation (MAD)4166.7
Skewness0.82428245
Sum1.1631198 × 108
Variance38168022
MonotonicityNot monotonic
2024-11-17T13:15:56.290344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.1%
16135.4 4
 
< 0.1%
49.9 4
 
< 0.1%
7140.5 3
 
< 0.1%
2510.4 3
 
< 0.1%
699.9 3
 
< 0.1%
6558.1 3
 
< 0.1%
2549.3 3
 
< 0.1%
1802.3 3
 
< 0.1%
14690.8 3
 
< 0.1%
Other values (13225) 13895
99.7%
ValueCountFrequency (%)
10.5 1
< 0.1%
22.6 1
< 0.1%
27.4 1
< 0.1%
29.3 1
< 0.1%
29.6 1
< 0.1%
33.9 2
< 0.1%
36.7 1
< 0.1%
42.5 1
< 0.1%
45.4 1
< 0.1%
46.3 2
< 0.1%
ValueCountFrequency (%)
29621.5 1
< 0.1%
29528.6 1
< 0.1%
29386.4 1
< 0.1%
29362.5 1
< 0.1%
29251.2 1
< 0.1%
29227.8 1
< 0.1%
29217.4 1
< 0.1%
29046.6 1
< 0.1%
29046.1 1
< 0.1%
28963.6 1
< 0.1%

OCEAN_DIST
Real number (ℝ)

Distinct13617
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31690.994
Minimum236.1
Maximum75744.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:56.624249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum236.1
5-th percentile5444.675
Q118079.35
median28541.75
Q344310.65
95-th percentile62611.415
Maximum75744.9
Range75508.8
Interquartile range (IQR)26231.3

Descriptive statistics

Standard deviation17595.079
Coefficient of variation (CV)0.55520756
Kurtosis-0.69038076
Mean31690.994
Median Absolute Deviation (MAD)12438.75
Skewness0.39314012
Sum4.4151893 × 108
Variance3.0958682 × 108
MonotonicityNot monotonic
2024-11-17T13:15:56.860075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21012 3
 
< 0.1%
42047 3
 
< 0.1%
55025.2 3
 
< 0.1%
34891 3
 
< 0.1%
13858.2 3
 
< 0.1%
22211.5 3
 
< 0.1%
61433.2 3
 
< 0.1%
15183.3 3
 
< 0.1%
28968.2 3
 
< 0.1%
33545.3 3
 
< 0.1%
Other values (13607) 13902
99.8%
ValueCountFrequency (%)
236.1 1
< 0.1%
308.8 1
< 0.1%
311.5 1
< 0.1%
376.7 1
< 0.1%
401 1
< 0.1%
435.7 1
< 0.1%
589.7 1
< 0.1%
623.4 1
< 0.1%
718.7 1
< 0.1%
725.5 1
< 0.1%
ValueCountFrequency (%)
75744.9 1
< 0.1%
75669 1
< 0.1%
75610.5 1
< 0.1%
75550 1
< 0.1%
75544 1
< 0.1%
75535.7 1
< 0.1%
75370 1
< 0.1%
75367.8 1
< 0.1%
75306.4 1
< 0.1%
75247.9 1
< 0.1%

WATER_DIST
Real number (ℝ)

High correlation 

Distinct13218
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11960.285
Minimum0
Maximum50399.8
Zeros108
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:57.142773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile401.47
Q12675.85
median6922.6
Q319200
95-th percentile36972.89
Maximum50399.8
Range50399.8
Interquartile range (IQR)16524.15

Descriptive statistics

Standard deviation11932.992
Coefficient of variation (CV)0.99771804
Kurtosis0.32899124
Mean11960.285
Median Absolute Deviation (MAD)5522
Skewness1.1380489
Sum1.6663069 × 108
Variance1.4239631 × 108
MonotonicityNot monotonic
2024-11-17T13:15:57.347015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108
 
0.8%
7.2 4
 
< 0.1%
3424 3
 
< 0.1%
11 3
 
< 0.1%
6047 3
 
< 0.1%
6252.7 3
 
< 0.1%
1831.1 3
 
< 0.1%
2292.4 3
 
< 0.1%
31855.7 3
 
< 0.1%
523.4 3
 
< 0.1%
Other values (13208) 13796
99.0%
ValueCountFrequency (%)
0 108
0.8%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
1 1
 
< 0.1%
2.1 1
 
< 0.1%
2.2 1
 
< 0.1%
2.3 1
 
< 0.1%
2.9 1
 
< 0.1%
3.6 1
 
< 0.1%
3.8 1
 
< 0.1%
ValueCountFrequency (%)
50399.8 1
< 0.1%
50279 1
< 0.1%
50206.2 1
< 0.1%
50162.5 1
< 0.1%
49988 1
< 0.1%
49941.7 1
< 0.1%
49840.9 1
< 0.1%
49798.2 1
< 0.1%
49768.7 1
< 0.1%
49693.8 1
< 0.1%

CNTR_DIST
Real number (ℝ)

High correlation 

Distinct13682
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68490.327
Minimum3825.6
Maximum159976.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:57.554886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3825.6
5-th percentile21199.575
Q142823.1
median65852.4
Q389358.325
95-th percentile132151.68
Maximum159976.5
Range156150.9
Interquartile range (IQR)46535.225

Descriptive statistics

Standard deviation32008.475
Coefficient of variation (CV)0.467343
Kurtosis-0.31393897
Mean68490.327
Median Absolute Deviation (MAD)23292.25
Skewness0.42759819
Sum9.5420724 × 108
Variance1.0245425 × 109
MonotonicityNot monotonic
2024-11-17T13:15:57.799329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43025.9 3
 
< 0.1%
79187.6 3
 
< 0.1%
14557.6 3
 
< 0.1%
25074 3
 
< 0.1%
93953.7 3
 
< 0.1%
48378.1 3
 
< 0.1%
88949.5 2
 
< 0.1%
88348.4 2
 
< 0.1%
60642.1 2
 
< 0.1%
35167.4 2
 
< 0.1%
Other values (13672) 13906
99.8%
ValueCountFrequency (%)
3825.6 1
< 0.1%
4509 1
< 0.1%
5454.2 1
< 0.1%
6146.5 1
< 0.1%
6189.3 1
< 0.1%
6275 1
< 0.1%
6360.4 1
< 0.1%
6468.6 1
< 0.1%
6561 1
< 0.1%
6631.4 1
< 0.1%
ValueCountFrequency (%)
159976.5 1
< 0.1%
159393.8 1
< 0.1%
159161.7 1
< 0.1%
159017.5 1
< 0.1%
158200.6 1
< 0.1%
157703.2 1
< 0.1%
157375.8 1
< 0.1%
157296.1 1
< 0.1%
157250.4 1
< 0.1%
157206.3 1
< 0.1%

SUBCNTR_DI
Real number (ℝ)

High correlation 

Distinct13642
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41115.047
Minimum1462.8
Maximum110553.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:58.080669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1462.8
5-th percentile8912.255
Q123996.25
median41109.9
Q353949.375
95-th percentile83551.61
Maximum110553.8
Range109091
Interquartile range (IQR)29953.125

Descriptive statistics

Standard deviation22161.826
Coefficient of variation (CV)0.53901983
Kurtosis-0.21126108
Mean41115.047
Median Absolute Deviation (MAD)15103.85
Skewness0.45414991
Sum5.7281484 × 108
Variance4.9114653 × 108
MonotonicityNot monotonic
2024-11-17T13:15:58.343430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60514.7 3
 
< 0.1%
45214.2 3
 
< 0.1%
37728.9 3
 
< 0.1%
44742.7 3
 
< 0.1%
25074 3
 
< 0.1%
14557.6 3
 
< 0.1%
44362.6 3
 
< 0.1%
45275 2
 
< 0.1%
29573.8 2
 
< 0.1%
47732.3 2
 
< 0.1%
Other values (13632) 13905
99.8%
ValueCountFrequency (%)
1462.8 1
< 0.1%
1602.8 1
< 0.1%
1619.1 1
< 0.1%
1632.4 1
< 0.1%
1680.8 1
< 0.1%
1840 1
< 0.1%
1877 1
< 0.1%
1898.9 1
< 0.1%
1928.3 1
< 0.1%
1964.8 1
< 0.1%
ValueCountFrequency (%)
110553.8 1
< 0.1%
109618.5 1
< 0.1%
109463.6 1
< 0.1%
109347.2 1
< 0.1%
108929.2 1
< 0.1%
108333.6 1
< 0.1%
108067.3 1
< 0.1%
107949.6 1
< 0.1%
107911.3 1
< 0.1%
107803.2 1
< 0.1%

HWY_DIST
Real number (ℝ)

Distinct13213
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7723.7707
Minimum90.2
Maximum48167.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:58.587997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90.2
5-th percentile759.46
Q12998.125
median6159.75
Q310854.2
95-th percentile20068.93
Maximum48167.3
Range48077.1
Interquartile range (IQR)7856.075

Descriptive statistics

Standard deviation6068.9361
Coefficient of variation (CV)0.78574784
Kurtosis1.1026945
Mean7723.7707
Median Absolute Deviation (MAD)3621.35
Skewness1.1162497
Sum1.0760757 × 108
Variance36831985
MonotonicityNot monotonic
2024-11-17T13:15:58.846971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2140.8 4
 
< 0.1%
1022.6 3
 
< 0.1%
857.4 3
 
< 0.1%
13816.9 3
 
< 0.1%
4589.9 3
 
< 0.1%
13752.4 3
 
< 0.1%
1848 3
 
< 0.1%
10656.3 3
 
< 0.1%
4522.1 3
 
< 0.1%
1582.4 3
 
< 0.1%
Other values (13203) 13901
99.8%
ValueCountFrequency (%)
90.2 1
< 0.1%
93.9 1
< 0.1%
96.5 1
< 0.1%
96.9 1
< 0.1%
101.7 1
< 0.1%
104.7 1
< 0.1%
105.9 1
< 0.1%
109 1
< 0.1%
110.6 1
< 0.1%
111 1
< 0.1%
ValueCountFrequency (%)
48167.3 1
< 0.1%
46641.4 1
< 0.1%
44755.9 1
< 0.1%
39633.5 1
< 0.1%
39484.1 1
< 0.1%
39266.3 1
< 0.1%
38701.1 1
< 0.1%
38398.5 1
< 0.1%
37222.8 1
< 0.1%
36922.9 1
< 0.1%

age
Real number (ℝ)

High correlation  Zeros 

Distinct96
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.669251
Minimum0
Maximum96
Zeros1003
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:59.050290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median26
Q346
95-th percentile67
Maximum96
Range96
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.153068
Coefficient of variation (CV)0.68971585
Kurtosis-0.65313121
Mean30.669251
Median Absolute Deviation (MAD)14
Skewness0.50501449
Sum427284
Variance447.45229
MonotonicityNot monotonic
2024-11-17T13:15:59.275740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1003
 
7.2%
16 511
 
3.7%
26 453
 
3.3%
21 447
 
3.2%
11 434
 
3.1%
36 363
 
2.6%
12 331
 
2.4%
10 330
 
2.4%
23 329
 
2.4%
31 308
 
2.2%
Other values (86) 9423
67.6%
ValueCountFrequency (%)
0 1003
7.2%
1 200
 
1.4%
2 68
 
0.5%
3 97
 
0.7%
4 71
 
0.5%
5 52
 
0.4%
6 138
 
1.0%
7 58
 
0.4%
8 59
 
0.4%
9 195
 
1.4%
ValueCountFrequency (%)
96 3
 
< 0.1%
94 1
 
< 0.1%
93 3
 
< 0.1%
92 9
0.1%
91 21
0.2%
90 16
0.1%
89 3
 
< 0.1%
88 11
0.1%
87 5
 
< 0.1%
86 9
0.1%

avno60plus
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.0 KiB
0.0
13724 
1.0
 
208

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41796
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13724
98.5%
1.0 208
 
1.5%

Length

2024-11-17T13:15:59.529027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T13:15:59.728143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13724
98.5%
1.0 208
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 27656
66.2%
. 13932
33.3%
1 208
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27656
66.2%
. 13932
33.3%
1 208
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27656
66.2%
. 13932
33.3%
1 208
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27656
66.2%
. 13932
33.3%
1 208
 
0.5%

month_sold
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6558283
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.0 KiB
2024-11-17T13:15:59.913721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3015235
Coefficient of variation (CV)0.49603495
Kurtosis-1.1199705
Mean6.6558283
Median Absolute Deviation (MAD)3
Skewness-0.0056676051
Sum92729
Variance10.900057
MonotonicityNot monotonic
2024-11-17T13:16:00.133759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 1387
10.0%
8 1275
9.2%
5 1245
8.9%
4 1234
8.9%
3 1224
8.8%
9 1218
8.7%
7 1210
8.7%
12 1161
8.3%
11 1159
8.3%
10 1057
7.6%
Other values (2) 1762
12.6%
ValueCountFrequency (%)
1 833
6.0%
2 929
6.7%
3 1224
8.8%
4 1234
8.9%
5 1245
8.9%
6 1387
10.0%
7 1210
8.7%
8 1275
9.2%
9 1218
8.7%
10 1057
7.6%
ValueCountFrequency (%)
12 1161
8.3%
11 1159
8.3%
10 1057
7.6%
9 1218
8.7%
8 1275
9.2%
7 1210
8.7%
6 1387
10.0%
5 1245
8.9%
4 1234
8.9%
3 1224
8.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.0 KiB
4.0
7625 
2.0
4110 
5.0
2002 
1.0
 
179
3.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41796
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 7625
54.7%
2.0 4110
29.5%
5.0 2002
 
14.4%
1.0 179
 
1.3%
3.0 16
 
0.1%

Length

2024-11-17T13:16:00.369983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T13:16:00.536425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 7625
54.7%
2.0 4110
29.5%
5.0 2002
 
14.4%
1.0 179
 
1.3%
3.0 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 13932
33.3%
0 13932
33.3%
4 7625
18.2%
2 4110
 
9.8%
5 2002
 
4.8%
1 179
 
0.4%
3 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 13932
33.3%
0 13932
33.3%
4 7625
18.2%
2 4110
 
9.8%
5 2002
 
4.8%
1 179
 
0.4%
3 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 13932
33.3%
0 13932
33.3%
4 7625
18.2%
2 4110
 
9.8%
5 2002
 
4.8%
1 179
 
0.4%
3 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 13932
33.3%
0 13932
33.3%
4 7625
18.2%
2 4110
 
9.8%
5 2002
 
4.8%
1 179
 
0.4%
3 16
 
< 0.1%

Interactions

2024-11-17T13:15:48.346370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.465991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.947824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.370517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.996594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.477844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.021371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.615078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.199503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.766409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.365726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.865244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.456093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.946836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.615706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:48.516194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.581447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.030546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.462608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.091631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.581881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.119214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.754740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.327781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.865328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.458748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.971666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.552686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.047348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.704881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:48.665096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.680199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.130639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.565073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.182899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.683759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.224189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.837423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.417026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.978614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.545497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.062968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.647079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.130496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.799540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:48.845614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.778271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.232392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.671859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.278441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.784381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.323672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.943018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.515625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.114829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.667035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.161930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.754181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.241804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.915922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:49.066909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.885895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.316978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.880920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.381198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.861780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.411007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.035799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.646809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.236616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.878606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.272790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.865755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.340673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.012100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:49.216910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:26.976923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.416939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.979052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.482715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.973606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.504609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.127914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.761305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.334761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.967325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.377346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.958573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.443132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.114765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:49.412075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.076921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.498435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.084367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.588995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.098937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.597333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.227805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.859386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.451557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.048855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.478992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.048853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.552210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.198880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:49.624768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.165322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.598720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.182050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.694438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.215738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.698422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.353284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.963296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.563189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.150647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.578293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.148582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.650151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.301597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:49.825222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.265160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.692380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.283210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.781307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.311206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.785107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.461483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.058115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.667990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.233437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.681469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.251460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.768426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.392713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.001788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.364891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.783546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.381059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.884962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.429842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.896116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.587608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.153919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.771430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.337136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.793899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.354077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.881807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.495635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.195537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.453499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.867362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.482955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:31.968274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.524295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:34.993623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.680305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.251961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.865951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.411851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:42.919043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.455335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:45.980486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.583823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.381322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.547856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:28.982731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.590685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.064056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.629197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.102642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.801815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.365323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:39.984627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.523442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.039406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.568221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.091492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.692757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.593025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.665966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.081843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.682150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.164637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.740093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.353165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.904063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.466137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.077609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.611279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.156057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.683475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.190041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.803086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.778287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.746999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.178851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.796486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.247908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.813076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.445876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:36.995570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.561203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.164326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.702391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.247079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.783353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.293183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:47.973753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:50.993414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:27.847977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:29.278935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:30.894486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:32.365352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:33.918242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:35.538180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:37.097550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:38.667232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:40.270812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:41.780929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:43.347551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:44.861995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:46.393044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-17T13:15:48.176711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-11-17T13:16:00.936521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CNTR_DISTHWY_DISTLATITUDELND_SQFOOTLONGITUDEOCEAN_DISTPARCELNORAIL_DISTSALE_PRCSPEC_FEAT_VALSUBCNTR_DITOT_LVG_AREAWATER_DISTageavno60plusmonth_soldstructure_quality
CNTR_DIST1.0000.042-0.698-0.153-0.8330.2750.6090.445-0.181-0.0410.6970.2390.584-0.5570.1970.0250.306
HWY_DIST0.0421.000-0.1080.070-0.1720.0290.012-0.0710.3270.109-0.1380.2200.190-0.0770.097-0.0100.124
LATITUDE-0.698-0.1081.0000.0330.7200.222-0.350-0.183-0.0200.026-0.093-0.260-0.5360.4350.303-0.0240.311
LND_SQFOOT-0.1530.0700.0331.0000.206-0.2380.036-0.0770.3260.388-0.2340.278-0.1860.2760.0180.0100.024
LONGITUDE-0.833-0.1720.7200.2061.000-0.469-0.445-0.3320.0030.007-0.371-0.268-0.8200.4960.215-0.0120.243
OCEAN_DIST0.2750.0290.222-0.238-0.4691.0000.1630.214-0.177-0.0130.443-0.0430.456-0.1150.205-0.0110.247
PARCELNO0.6090.012-0.3500.036-0.4450.1631.0000.279-0.0840.0580.3840.1650.270-0.3230.3190.0130.244
RAIL_DIST0.445-0.071-0.183-0.077-0.3320.2140.2791.000-0.075-0.0120.4300.1310.183-0.2300.1480.0120.099
SALE_PRC-0.1810.327-0.0200.3260.003-0.177-0.084-0.0751.0000.388-0.4050.6950.005-0.1890.0340.0370.299
SPEC_FEAT_VAL-0.0410.1090.0260.3880.007-0.0130.058-0.0120.3881.000-0.1090.349-0.023-0.0110.000-0.0180.100
SUBCNTR_DI0.697-0.138-0.093-0.234-0.3710.4430.3840.430-0.405-0.1091.0000.0150.163-0.3910.2670.0170.226
TOT_LVG_AREA0.2390.220-0.2600.278-0.268-0.0430.1650.1310.6950.3490.0151.0000.205-0.3760.0660.0120.156
WATER_DIST0.5840.190-0.536-0.186-0.8200.4560.2700.1830.005-0.0230.1630.2051.000-0.3430.1290.0110.210
age-0.557-0.0770.4350.2760.496-0.115-0.323-0.230-0.189-0.011-0.391-0.376-0.3431.0000.123-0.0430.173
avno60plus0.1970.0970.3030.0180.2150.2050.3190.1480.0340.0000.2670.0660.1290.1231.0000.0300.106
month_sold0.025-0.010-0.0240.010-0.012-0.0110.0130.0120.037-0.0180.0170.0120.011-0.0430.0301.0000.014
structure_quality0.3060.1240.3110.0240.2430.2470.2440.0990.2990.1000.2260.1560.2100.1730.1060.0141.000

Missing values

2024-11-17T13:15:51.262193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-17T13:15:51.744857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LATITUDELONGITUDEPARCELNOSALE_PRCLND_SQFOOTTOT_LVG_AREASPEC_FEAT_VALRAIL_DISTOCEAN_DISTWATER_DISTCNTR_DISTSUBCNTR_DIHWY_DISTageavno60plusmonth_soldstructure_quality
025.891031-80.1605613.074472e-312440000.09375.01753.00.02815.912811.4347.642815.337742.215954.967.00.08.04.0
125.891324-80.1539683.074472e-312349000.09375.01715.00.04359.110648.4337.843504.937340.518125.063.00.09.04.0
225.891334-80.1537403.074472e-312800000.09375.02276.049206.04412.910574.1297.143530.437328.718200.561.00.02.04.0
325.891765-80.1526573.074472e-312988000.012450.02058.010033.04585.010156.50.043797.537423.218514.463.00.09.04.0
425.891825-80.1546393.074472e-312755000.012800.01684.016681.04063.410836.8326.643599.737550.817903.442.00.07.04.0
525.892060-80.1613543.074472e-312630000.09900.01531.02978.02391.413017.0188.943135.138176.215687.241.00.02.04.0
625.892473-80.1572173.074472e-3121020000.010387.01753.023116.03277.411667.80.043598.737973.917068.263.00.02.05.0
725.893019-80.1574263.074472e-312850000.010272.01663.034933.03112.411718.110.543780.838198.316989.921.00.09.04.0
825.893046-80.1615563.074472e-312250000.09375.01493.011668.02081.813043.851.543481.738542.015623.356.00.03.04.0
925.893050-80.1580483.074472e-3121220000.013803.03077.034580.02937.711917.79.743730.138235.216787.063.00.011.05.0
LATITUDELONGITUDEPARCELNOSALE_PRCLND_SQFOOTTOT_LVG_AREASPEC_FEAT_VALRAIL_DISTOCEAN_DISTWATER_DISTCNTR_DISTSUBCNTR_DIHWY_DISTageavno60plusmonth_soldstructure_quality
1392225.610857-80.3810491.507053e-311245000.08000.01731.013686.06742.123833.310397.086423.635873.5905.433.00.07.02.0
1392325.611927-80.3811091.507053e-311265000.08000.01346.07944.06629.023890.110669.986168.435582.8908.813.00.09.02.0
1392425.612170-80.3828481.511525e-311230000.07500.01539.03474.06051.224470.311159.286523.535879.11478.733.00.05.02.0
1392525.780463-80.2607256.488077e-313315000.09062.02261.02856.04221.719831.43030.722394.511189.91864.368.00.02.04.0
1392625.782801-80.2607606.488072e-313215000.09605.01640.06856.03665.320593.92918.822467.212042.31022.369.00.03.02.0
1392725.783130-80.2597956.488072e-313275000.06780.0967.06580.03844.520568.03252.422175.912150.1917.416.00.04.04.0
1392825.783585-80.2603546.488072e-313340000.07500.01854.02544.03593.620791.93077.722375.112316.8738.226.00.05.04.0
1392925.783793-80.2561266.488072e-313287500.08460.01271.02064.04143.220307.93588.420966.912433.0743.716.00.07.04.0
1393025.784007-80.2575426.488072e-313315000.07500.01613.03136.03986.920542.63589.121475.612458.0626.116.00.08.04.0
1393125.784387-80.2589016.488072e-313250000.08833.01867.0266.03793.920859.63421.021928.612599.0474.762.00.011.04.0